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All Journal International Journal of Mechanical, Industrial and Control Systems Engineering
Yulaikha Maratullatifah
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Computer Science & IT Engineering

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Multi Objective Evolutionary Optimization of Additive Manufacturing Process Parameters for Enhanced Mechanical Performance and Surface Integrity Yulaikha Maratullatifah; Dwi Utari Iswavigra; Very Dwi Setiawan; Mursalim Mursalim; Budi Wibowo
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i1.400

Abstract

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.
Hybrid Reinforcement Learning and Robust Adaptive Control Strategy for Autonomous Manufacturing Systems under Uncertain and Dynamic Production Environments Irlon Irlon; Teguh Muryanto; Sayyid Jamal Al Din; Dwi Utari Iswavigra; Yulaikha Maratullatifah; Very Dwi Setiawan
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.403

Abstract

This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning techniques like RL, deep Q-networks (DQN), and deep deterministic policy gradient (DDPG), enhance decision-making capabilities in dynamic production environments. The study develops a hybrid RL robust control framework and tests it in both simulation and real-world scenarios. Performance metrics, including production efficiency, system stability, and adaptability, are assessed under various disturbance conditions, such as machine failures and fluctuating demands. The hybrid model significantly outperforms traditional PID control in terms of efficiency and stability, demonstrating faster convergence and better adaptability in dynamic environments. Statistical analysis confirms the superiority of the hybrid system over standalone RL models and traditional PID control. This model’s scalability and adaptability make it a promising solution for Industry 4.0 applications, addressing key challenges in real-world manufacturing systems by ensuring computational efficiency and the ability to manage large-scale data. The findings contribute to the development of more robust and efficient control strategies for autonomous manufacturing systems in uncertain environments.
Co-Authors BUDI WIBOWO Dwi Utari Iswavigra Irlon Irlon Mursalim Mursalim Sayyid Jamal Al Din Teguh Muryanto Very Dwi Setiawan